Pathology of the malignant colorectal polyp: Issues in morphologic criteria and recommendations from the Italian Group of Gastrointestinal Pathologists
Bibliographic record
Abstract
Malignant colorectal polyps (MCPs) are early-stage colorectal cancers (CRC) generally diagnosed following endoscopic removal of otherwise bland lesions. Due to nodal metastatic potential and risk of residual disease, diagnosis and risk stratification of MCPs are critical for determining appropriate clinical management, which can range from clinical/endoscopic/imaging follow-up to radical surgery with locoregional lymphadenectomy. Although a dedicated multidisciplinary team should discuss this decision, the MCP histopathologic assessment is crucial and raises several issues. Following productive discussions that occurred in dedicated meetings and educational activities, the Italian Group of Gastrointestinal Pathologists have developed these recommendations for the histopathologic assessment and multidisciplinary management of MCPs, addressing diagnostic challenges and proposing standardized criteria. This document is based on a comprehensive review of the literature and opinions from pathologists with dedicated gastrointestinal experience. Key topics include pre-analytical specimen handling, histopathologic criteria for MCP diagnosis, and assessment of histopathologic features associated with MCP high-risk behavior. The role and integration of features inferred from advanced CRCs, such as mismatch repair protein status testing, are also addressed and discussed. The proposed recommendations aim to improve MCP risk stratification by structuring and standardizing the histopathologic approach. The adoption of a multidisciplinary team discussion remains crucial for MCP patient management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".